一种新型并行LSTM网络食品安全检测方法

2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2020-10-01 DOI:10.1109/DCABES50732.2020.00072

Qing-An Huang, Jun Sun, Jianhua Wang

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引用次数: 0

摘要

食品安全是关系民生的重大问题。随着互联网时代的到来，很多人选择在网上订餐，网络食品安全监管面临着巨大的挑战。通过对第三方平台的点评数据进行分析，构建食品安全违规与风险评价数据集。为了找到评论数据与网络食品风险等级之间的关系，提出了一种新的并行分布式长短期记忆网络模型来预测商家的风险值，并建立了网络外卖商家预警系统。

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A Novel Parallelized LSTM For Detecting Internet Food Safety

Food safety is a major problem concerning people's livelihood. With the advent of the era of Internet, many people choose to order food online, while the regulation of online food safety is faced with enormous challenges. Through the analysis of the comments data from third-party platform, a food safety evaluation dataset of violation and risk is constructed. In order to find the relationship between the comment data and risks level of online food, a novel parallelized distributed long and short term memory network model is proposed to predict the risk value of merchants, and an early warning system for network takeout merchants is established.

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2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

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